FUNOVA Preprocessing QC statistics ¶

January 2025¶

In [19]:
import os
NOVA_HOME = "/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA"
NOVA_DATA_HOME = '/home/labs/hornsteinlab/Collaboration/FUNOVA'

LOGS_PATH = os.path.join(NOVA_DATA_HOME, "outputs/preprocessing/logs/")
PLOT_PATH = os.path.join(NOVA_DATA_HOME, "outputs/logs/")

os.chdir(NOVA_HOME)

import pandas as pd
import contextlib
import io
from IPython.display import display, Javascript
import seaborn as sns
from tools.preprocessing_tools.qc_reports.qc_utils import log_files_qc, run_validate_folder_structure, display_diff, sample_and_calc_variance, \
                                                show_site_survival_dapi_brenner, show_site_survival_dapi_cellpose, \
                                                show_site_survival_dapi_tiling, show_site_survival_target_brenner, \
                                                calc_total_sums, plot_filtering_heatmap, show_total_sum_tables, \
                                                plot_cell_count, plot_catplot, plot_hm_combine_batches, plot_hm, \
                                                run_calc_hist_new, plot_marker_data, find_bad_wells, create_marker_info_df, \
                                                show_site_survival_by_brenner_on_dapi_tiles, show_site_survival_target_brenner_tiles
                                                
from tools.preprocessing_tools.qc_reports.qc_config import (
    funova_markers as markers,
    funova_cell_lines as cell_lines,
    funova_cell_lines_to_cond as cell_lines_to_cond,
    funova_cell_lines_for_disp as cell_lines_for_disp,
    funova_reps as reps,
    funova_line_colors as line_colors,
    funova_lines_order as lines_order,
    funova_custom_palette as custom_palette,
    funova_expected_dapi_raw as expected_dapi_raw,
    funova_panels as panels,
    funova_marker_info as marker_info
)

from tools.show_images_utils import *
%load_ext autoreload
%autoreload 2

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload
In [20]:
# choose batches
batches = ['Batch1', 'Batch2']#, 'batch2', 'batch3']
batches
Out[20]:
['Batch1', 'Batch2']

I have created a folder called 'Batch1' in the logs dir and put all files inside¶

In [21]:
validate_antibody = False
In [22]:
df = log_files_qc(LOGS_PATH, batches, only_wt_cond = False)

reading logs of Batch1

Total of 1 files were read.
Before dup handeling  (171516, 22)
After duplication removal #1: (171516, 23)
After duplication removal #2: (171516, 23)
In [23]:
df[['cell_line', 'marker', 'cell_line_cond']] = df[['cell_line', 'marker', 'cell_line_cond']].apply(lambda x: x.str.replace('_', '-', regex=False))
In [24]:
df['filename'] = df['filename'].str.split('-').str[0]
df['site_num'] = df['site_num'].str.split('-').str[0]
In [25]:
df_dapi = df[df.marker=='DAPI']
df_target = df[df.marker!='DAPI']

Actual Files Validation¶

Raw Files Validation¶

  1. How many site tiff files do we have in each folder?
  2. Are all existing files valid? (tif, at least 2049kB, not corrupetd)
In [26]:
root_directory_raw = os.path.join(NOVA_DATA_HOME, 'input', 'images', 'raw')
In [27]:
df_metadata = extract_image_metadata(root_directory_raw, FILE_EXTENSION = '.tiff', KEY_BATCH = 'Batch')
In [28]:
# root_path = "/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/"
# marker_info_df = create_marker_info_df(root_path)
In [29]:
batches_raw = [batch.replace("_16bit_no_downsample","") for batch in batches]
raws = run_validate_folder_structure(root_directory_raw, False, panels, markers.copy(), PLOT_PATH, marker_info,
                                    cell_lines_to_cond, reps, cell_lines_for_disp, expected_dapi_raw,
                                     batches=batches_raw, fig_width=8,fig_height = 40,
                                    expected_count=100, validate_antibody = validate_antibody)

Batch1
Folder structure is valid.
No bad files are found.
Total Sites:  118400

========
Batch2
Folder structure is valid.
No bad files are found.
Total Sites:  118400

========
====================

Processed Files Validation¶

  1. How many site npy files do we have in each folder? -> How many sites survived the pre-processing?
  2. Are all existing files valid? (at least 100kB, npy not corrupted)
In [30]:
root_directory_proc = os.path.join(NOVA_DATA_HOME, 'input', 'images', 'processed')
procs = run_validate_folder_structure(root_directory_proc, True, panels, markers, PLOT_PATH, marker_info,
                                    cell_lines_to_cond, reps, cell_lines_for_disp, expected_dapi_raw,
                                     batches=batches, fig_width=8,fig_height = 40,
                                    expected_count=100, validate_antibody = validate_antibody)

Batch1
Folder structure is valid.
495 files are bad:
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch1/Control-1001733/Untreated/impaired-Autophagosome, rep1_r05c07f34-ch3t1_panelD_Control-1001733_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch1/Control-1001733/Untreated/UPR-ATF4, rep1_r05c05f89-ch3t1_panelC_Control-1001733_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch1/Control-1001733/Untreated/UPR-ATF4, rep1_r05c05f23-ch3t1_panelC_Control-1001733_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch1/Control-1001733/Untreated/UPR-ATF4, rep2_r05c06f20-ch3t1_panelC_Control-1001733_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch1/Control-1001733/Untreated/UPR-ATF4, rep1_r05c05f97-ch3t1_panelC_Control-1001733_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch1/Control-1001733/Untreated/UPR-ATF4, rep1_r05c05f81-ch3t1_panelC_Control-1001733_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch1/Control-1001733/Untreated/UPR-ATF4, rep1_r05c05f20-ch3t1_panelC_Control-1001733_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch1/Control-1001733/Untreated/UPR-ATF4, rep2_r05c06f49-ch3t1_panelC_Control-1001733_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch1/Control-1001733/Untreated/UPR-ATF4, rep2_r05c06f48-ch3t1_panelC_Control-1001733_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch1/Control-1001733/Untreated/UPR-ATF4, rep1_r05c05f33-ch3t1_panelC_Control-1001733_processed.npy small size (0.128 kB)
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/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch1/Control-1001733/Untreated/UPR-ATF4, rep2_r05c06f30-ch3t1_panelC_Control-1001733_processed.npy small size (0.128 kB)
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Total Sites:  83934

========
Batch2
Folder structure is valid.
413 files are bad:
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/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch2/TDP--43-N390D-1005373/stress/DNA-damage-P53BP1, rep2_r14c20f83-ch2t1_panelJ_TDP--43-N390D-1005373_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch2/TDP--43-N390D-1005373/stress/DNA-damage-P53BP1, rep2_r14c20f07-ch2t1_panelJ_TDP--43-N390D-1005373_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch2/TDP--43-N390D-1005373/stress/DNA-damage-P53BP1, rep2_r14c20f29-ch2t1_panelJ_TDP--43-N390D-1005373_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch2/TDP--43-N390D-1005373/stress/DNA-damage-P53BP1, rep2_r14c20f66-ch2t1_panelJ_TDP--43-N390D-1005373_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch2/TDP--43-N390D-1005373/stress/DNA-damage-P53BP1, rep2_r14c20f78-ch2t1_panelJ_TDP--43-N390D-1005373_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch2/TDP--43-N390D-1005373/stress/Necroptosis-HMGB1, rep1_r14c17f11-ch2t1_panelI_TDP--43-N390D-1005373_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch2/TDP--43-N390D-1005373/stress/Nuclear-speckles-SON, rep1_r14c13f19-ch2t1_panelG_TDP--43-N390D-1005373_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch2/TDP--43-N390D-1005373/stress/Nuclear-speckles-SON, rep1_r14c13f78-ch2t1_panelG_TDP--43-N390D-1005373_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch2/TDP--43-N390D-1005373/stress/TDP-43, rep2_r14c14f11-ch3t1_panelG_TDP--43-N390D-1005373_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch2/TDP--43-N390D-1005373/stress/TDP-43, rep2_r14c14f04-ch3t1_panelG_TDP--43-N390D-1005373_processed.npy small size (0.128 kB)
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/processed/Batch2/TDP--43-N390D-1005373/stress/TDP-43, rep1_r14c13f04-ch3t1_panelG_TDP--43-N390D-1005373_processed.npy small size (0.128 kB)
Total Sites:  64620

========
====================

Difference between Raw and Processed¶

In [31]:
display_diff(batches, raws, procs, PLOT_PATH, fig_width=8, fig_height = 40)

Batch1

========
Batch2

========

Variance in each batch (of processed files)¶

In [32]:
for batch in batches:
    with contextlib.redirect_stdout(io.StringIO()):
        var = sample_and_calc_variance(root_directory_proc, batch, 
                                       sample_size_per_markers=200, cond_count=1, rep_count=len(reps), 
                                       num_markers=len(markers))
    print(f'{batch} var: ',var)

/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)

Batch1 var:  nan

/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)
/home/labs/hornsteinlab/Collaboration/NOVA_GAL/NOVA/tools/preprocessing_tools/qc_reports/qc_utils.py:29: RuntimeWarning: Mean of empty slice.
  tiles_var = np.var(x, axis=(1,2)).mean()
/home/labs/hornsteinlab/galavir/.conda/envs/nova/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)

Batch2 var:  nan

Preprocessing Filtering qc¶

By order of filtering

1. % site survival after Brenner on DAPI channel¶

Percentage out of the total sites

In [33]:
dapi_filter_by_brenner = show_site_survival_dapi_brenner(df_dapi,batches, line_colors, panels,
                                                        figsize=(6,18), reps=reps, vmax=100)
In [34]:
show_images(df_metadata, marker='DAPI', show_DAPI=False, batch=2, panel='L', cell_line='C9orf72-HRE-1008566', rep=1,
            condition='stress', funova=True, samples=5)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

1
Img shape: (1024, 1024)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch2/C9orf72-HRE-1008566/panelL/stress/rep1/DAPI/r12c23f61-ch1t1.tiff
2
Img shape: (1024, 1024)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch2/C9orf72-HRE-1008566/panelL/stress/rep1/DAPI/r12c23f07-ch1t1.tiff
3
Img shape: (1024, 1024)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch2/C9orf72-HRE-1008566/panelL/stress/rep1/DAPI/r12c23f81-ch1t1.tiff
4
Img shape: (1024, 1024)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch2/C9orf72-HRE-1008566/panelL/stress/rep1/DAPI/r12c23f01-ch1t1.tiff
5
Img shape: (1024, 1024)

/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch2/C9orf72-HRE-1008566/panelL/stress/rep1/DAPI/r12c23f72-ch1t1.tiff
In [35]:
show_images(df_metadata, marker='DAPI', show_DAPI=False, batch=2, panel='D', cell_line='Control-1017118', rep=1,
            condition='stress', funova=True, samples=5)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

1
Img shape: (1024, 1024)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch2/Control-1017118/panelD/stress/rep1/DAPI/r15c07f66-ch1t1.tiff
2
Img shape: (1024, 1024)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch2/Control-1017118/panelD/stress/rep1/DAPI/r15c07f59-ch1t1.tiff
3
Img shape: (1024, 1024)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
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/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch2/Control-1017118/panelD/stress/rep1/DAPI/r15c07f46-ch1t1.tiff
In [ ]:
# temp1 = get_specific_imgs(dfb, marker='DAPI', batch=2,cell_line='Control_1017118', rep=1, condition='stress')
# temp = temp1.loc[temp1.Panel =='D'].Target_Sharpness_Brenner
# temp1 = temp1.Target_Sharpness_Brenner
# plt.hist(temp1)
# plt.hist(temp)

2. % Site survival after Cellpose¶

Percentage out of the sites that passed the previous filter. In parenthesis are absolute values.

A site will be filtered out if Cellpose found 0 cells in it.

In [37]:
dapi_filter_by_cellpose = show_site_survival_dapi_cellpose(df_dapi, batches, dapi_filter_by_brenner, 
                                                           line_colors, panels, figsize=(6,18), reps=reps)

3. % Site survival by tiling¶

Percentage out of the sites that passed the previous filter. In parenthesis are absolute values.

A site will be filtered out if after tiling, no tile is containing at least 85% of a cell that Cellpose detected.

In [38]:
dapi_filter_by_tiling=show_site_survival_dapi_tiling(df_dapi, batches, dapi_filter_by_cellpose, 
                                                     line_colors, panels, figsize=(6,18), reps=reps)

4. % Site survival after Brenner on DAPI's tiles¶

Percentage out of the sites that passed the previous filter. In parenthesis are absolute values (if different than the percentages).

In [39]:
dapi_filter_by_brenner_tiles=show_site_survival_by_brenner_on_dapi_tiles(df_dapi, batches, dapi_filter_by_tiling, 
                                                     line_colors, panels, figsize=(6,18), reps=reps)

5. % Site survival after Brenner on target channel¶

Percentage out of the sites that passed the previous filter. In parenthesis are absolute values (if different than the percentages).

In [40]:
show_site_survival_target_brenner(df_dapi, df_target, dapi_filter_by_brenner_tiles,
                                 figsize=(6,24), markers=markers)
In [41]:
df_dapi.iloc[np.where(df_dapi['n_valid_tiles']-df_dapi['n_valid_tiles_after_tiles_brenner'])][['n_valid_tiles','n_valid_tiles_after_tiles_brenner']]
Out[41]:
n_valid_tiles n_valid_tiles_after_tiles_brenner
61865 2 0.0
61789 3 2.0
61609 2 0.0
61606 1 0.0
61633 2 0.0
... ... ...
118993 1 0.0
118990 1 0.0
119014 2 0.0
118984 1 0.0
119013 1 0.0

5557 rows × 2 columns

In [42]:
## Images that were completely removed and their corresponding DAPI 
show_images(df_metadata, marker = 'Senescence-signaling', show_DAPI = True, 
            batch = 1, cell_line ='Control-1048087', 
            condition = 'stress', rep=2, funova = True, samples = 5)

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--------------------------------
In [43]:
show_images(df_metadata, marker='Parthanatos-early', show_DAPI=False, batch=2, panel='H', cell_line='TDP--43-G348V-1057052', 
            rep=2, condition='Untreated', funova=True, samples=5)

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In [44]:
show_images(df_metadata, marker='Protein-degradation', show_DAPI=False, batch=2, panel='L', cell_line='C9orf72-HRE-981344', rep=1,
            condition='stress', funova=True, samples=5)

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Find non valid wells

In [45]:
dfb = pd.read_csv("/home/labs/hornsteinlab/Collaboration/FUNOVA/outputs/preprocessing/brenner/raw_metrics280125_exp3.csv")
# Extract the panel (e.g., "panelD") from the Path column
dfb['Panel'] = dfb['Path'].str.extract(r'/panel([A-Z])/', expand=True)
dfb['image_id'] = dfb['Path'].apply(lambda path: path.split('/')[-1].split('.')[0])
find_bad_wells(dfb, threshold=50000, percentage_filter=70)
Out[45]:
Panel Condition CellLine Rep Batch Count_table1 Count_table2 Percentage Combination Marker
0 J stress C9orf72-HRE_981344 rep1 Batch1 221 300 73.666667 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... NaN
1 L stress C9orf72-HRE_1008566 rep1 Batch2 285 300 95.000000 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... NaN
2 L stress C9orf72-HRE_981344 rep1 Batch2 268 300 89.333333 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... NaN
3 NaN Untreated TDP--43-G348V_1057052 rep2 Batch2 84 100 84.000000 ['Condition', 'CellLine', 'Rep', 'Batch', 'Mar... Parthanatos-early
4 NaN stress C9orf72-HRE_1008566 rep1 Batch2 96 100 96.000000 ['Condition', 'CellLine', 'Rep', 'Batch', 'Mar... Protein-degradation
5 NaN stress C9orf72-HRE_1008566 rep1 Batch2 94 100 94.000000 ['Condition', 'CellLine', 'Rep', 'Batch', 'Mar... Senescence-signaling
6 NaN stress C9orf72-HRE_981344 rep1 Batch1 91 100 91.000000 ['Condition', 'CellLine', 'Rep', 'Batch', 'Mar... DNA-damage-P53BP1
7 NaN stress C9orf72-HRE_981344 rep1 Batch1 77 100 77.000000 ['Condition', 'CellLine', 'Rep', 'Batch', 'Mar... Neuronal-activity
8 NaN stress C9orf72-HRE_981344 rep1 Batch2 90 100 90.000000 ['Condition', 'CellLine', 'Rep', 'Batch', 'Mar... Protein-degradation
9 NaN stress C9orf72-HRE_981344 rep1 Batch2 91 100 91.000000 ['Condition', 'CellLine', 'Rep', 'Batch', 'Mar... Senescence-signaling
10 NaN stress C9orf72-HRE_981344 rep2 Batch1 78 100 78.000000 ['Condition', 'CellLine', 'Rep', 'Batch', 'Mar... Nuclear-speckles-SC35
11 NaN stress C9orf72-HRE_981344 rep2 Batch2 79 100 79.000000 ['Condition', 'CellLine', 'Rep', 'Batch', 'Mar... DNA-damage-P53BP1
12 D stress Control_1017118 rep1 Batch2 92 100 92.000000 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... DAPI
13 F stress C9orf72-HRE_981344 rep2 Batch1 78 100 78.000000 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... Nuclear-speckles-SC35
14 H Untreated TDP--43-G348V_1057052 rep2 Batch2 84 100 84.000000 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... Parthanatos-early
15 J stress C9orf72-HRE_981344 rep1 Batch1 91 100 91.000000 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... DNA-damage-P53BP1
16 J stress C9orf72-HRE_981344 rep1 Batch1 77 100 77.000000 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... Neuronal-activity
17 J stress C9orf72-HRE_981344 rep2 Batch2 79 100 79.000000 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... DNA-damage-P53BP1
18 L stress C9orf72-HRE_1008566 rep1 Batch2 95 100 95.000000 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... DAPI
19 L stress C9orf72-HRE_1008566 rep1 Batch2 96 100 96.000000 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... Protein-degradation
20 L stress C9orf72-HRE_1008566 rep1 Batch2 94 100 94.000000 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... Senescence-signaling
21 L stress C9orf72-HRE_981344 rep1 Batch2 87 100 87.000000 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... DAPI
22 L stress C9orf72-HRE_981344 rep1 Batch2 90 100 90.000000 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... Protein-degradation
23 L stress C9orf72-HRE_981344 rep1 Batch2 91 100 91.000000 ['Panel', 'Condition', 'CellLine', 'Rep', 'Bat... Senescence-signaling
In [46]:
# thresholds = {
#     "Stress_initiation": (0, 99),
#     "Aberrant_splicing": (1, 97),
#     "Autophagy": (5, 97),
#     "DAPI": (0.2, 95),
#     "Apoptosis": (0.27, 95),
#     "impaired_Autophagosome": (5, 99),
#     "Cytoskeleton": (10, 99.8),
#     "DNA_damage_P53BP1": (0.3, 90),
#     "DNA_damage_pH2Ax": (7, 85),
#     "mature_Autophagosome": (2, 98),
#     "Necrosis": (4, 95),
#     "Neuronal_activity": (5, 90),
#     "Nuclear_speckles_SC35": (0.2, 85),
#     "Nuclear_speckles_SON": (0.2, 85),
#     "Parthanatos_early": (0, 85),
#     "Parthanatos_late": (5, 95),
#     "Protein_degradation": (5, 92),
#     "Senescence_signaling": (10, 90),
#     "Splicing_factories": (0, 95),
#     "TDP-43": (5, 99.2),
#     "Ubiquitin_levels": (2, 90),
#     "UPR_ATF4": (2, 99.7), 
#     "UPR_ATF6": (3, 100),
#     "UPR_IRE1a": (2, 98),
#     "Necroptosis_pMLKL": (5, 90),
#     "Necroptosis_HMGB1": (0.01, 90),
# }
# # Extended batch data with new entries
# data = [
#     (2, 'DNA_damage_pH2Ax', 1, 1017118, 'Untreated'),
#     (2, 'Parthanatos_early', 1, 1048087, 'stress'),
#     (2, 'DNA_damage_pH2Ax', 2, 1008566, 'stress'),
#     (2, 'Parthanatos_early', 1, 981344, 'stress'),
#     (2, 'Nuclear_speckles_SON', 1, 981344, 'stress'),
#     (1, 'Autophagy', 1, 1057052, 'stress'),
#     (1, 'impaired_Autophagosome', 1, 1057052, 'stress'),
#     (1, 'UPR_IRE1a', 1, 1057052, 'stress'),
#     (1, 'Ubiquitin_levels', 1, 1057052, 'stress'),
#     (1, 'UPR_IRE1a', 1, 981344, 'stress'),
#     (1, 'Ubiquitin_levels', 1, 981344, 'stress'),
#     (1, 'DNA_damage_P53BP1', 1, 981344, 'stress'),
#     (1, 'Neuronal_activity', 1, 981344, 'stress'),
#     (1, 'Necroptosis_HMGB1', 1, 981344, 'stress'),
#     (1, 'Necrosis', 1, 981344, 'stress'),
#     (1, 'Nuclear_speckles_SC35', 2, 981344, 'stress'),
#     (1, 'Splicing_factories', 2, 981344, 'stress'),
#     (1, 'Aberrant_splicing', 2, 981344, 'stress'),
#     (1, 'Parthanatos_late', 2, 981344, 'stress'),
#     (1, 'Senescence_signaling', 2, 1048087, 'stress'),
#     (1, 'Apoptosis', 2, 981344, 'stress'),
#     (1, 'Necroptosis_pMLKL', 2, 981344, 'stress'),
#     (2, 'Autophagy', 1, 1017118, 'stress'),
#     (2, 'impaired_Autophagosome', 1, 1017118, 'stress'),
#     (2, 'UPR_IRE1a', 1, 1048087, 'Untreated'),
#     (2, 'UPR_IRE1a', 1, 1008566, 'stress'),
#     (2, 'Ubiquitin_levels', 1, 1048087, 'Untreated'),
#     (2, 'Ubiquitin_levels', 1, 1008566, 'stress'),
#     (2, 'DNA_damage_P53BP1', 1, 1017118, 'stress'),
#     (2, 'Neuronal_activity', 1, 1017118, 'stress'),
#     (2, 'DNA_damage_P53BP1', 2, 981344, 'stress'),
#     (2, 'Neuronal_activity', 2, 981344, 'stress'),
#     (2, 'DNA_damage_P53BP1', 1, 981344, 'stress'),
#     (2, 'Neuronal_activity', 1, 981344, 'stress'),
#     (2, 'DNA_damage_P53BP1', 1, 1057052, 'stress'),
#     (2, 'Neuronal_activity', 1, 1057052, 'stress'),
#     (2, 'DNA_damage_P53BP1', 1, 1005373, 'stress'),
#     (2, 'Neuronal_activity', 1, 1005373, 'stress'),
#     (2, 'DNA_damage_P53BP1', 2, 1057052, 'stress'),
#     (2, 'Neuronal_activity', 2, 1057052, 'stress'),
#     (2, 'Necroptosis_HMGB1', 1, 981344, 'Untreated'),
#     (2, 'Necrosis', 1, 981344, 'Untreated'),
#     (2, 'Necroptosis_HMGB1', 2, 981344, 'stress'),
#     (2, 'Necrosis', 2, 981344, 'stress'),
#     (2, 'Necroptosis_HMGB1', 2, 1001733, 'stress'),
#     (2, 'Necrosis', 2, 1001733, 'stress'),
#     (2, 'Necroptosis_HMGB1', 2, 1005373, 'stress'),
#     (2, 'Necrosis', 2, 1005373, 'stress')
# ]

# # Cell line ID mapping
# cell_line_mapping = {
#     1008566: 'C9orf72-HRE_1008566',
#     981344: 'C9orf72-HRE_981344',
#     1001733: 'Control_1001733',
#     1017118: 'Control_1017118',
#     1025045: 'Control_1025045',
#     1048087: 'Control_1048087',
#     1057052: 'TDP--43-G348V_1057052',
#     1005373: 'TDP--43-N390D_1005373'
# }

# # Convert to DataFrame (renamed to `marker_df`)
# marker_df = pd.DataFrame(data, columns=['Batch', 'Name', 'Rep', 'CellLineID', 'Condition'])

# lower_thresh_DAPI, upper_thresh_DAPI = thresholds['DAPI']
# cb = get_specific_imgs(dfb, marker='DAPI')
# cb = cb.loc[(cb.Batch == 'Batch1') | (cb.Batch == 'Batch2')].Target_Sharpness_Brenner.values
# lower_p_DAPI = np.percentile(cb, lower_thresh_DAPI)
# upper_p_DAPI = np.percentile(cb, upper_thresh_DAPI)

# # Iterate over each row
# for _, row in marker_df.iterrows():
#     batch, marker, rep, cell_line_id, condition = row

#     # Match Cell Line Name
#     cell_line = cell_line_mapping.get(cell_line_id, f'Unknown_{cell_line_id}')

#     # Fetch images based on specific conditions
#     cb1 = get_specific_imgs(dfb, marker=marker, batch=batch, rep=rep, 
#                            cell_line=cell_line, condition=condition)
    
#     dapi_paths = cb1.Path.apply(lambda p: get_dapi_path(p, marker1=marker, marker2='DAPI', funova=True))
#     dfDAPI = dfb.loc[dfb['Path'].isin(dapi_paths)].Target_Sharpness_Brenner.values
#     cb1 = cb1.Target_Sharpness_Brenner.values

#     # Print statistics dynamically for the specific selection
#     print(f"Stats for marker: {marker}, batch: {batch}, rep: {rep}, cell_line: {cell_line}, condition: {condition}")
#     print(f"Mean: {np.mean(cb1):.2f}, Median: {np.median(cb1):.2f}, "
#           f"10th Percentile: {np.percentile(cb1, 10):.2f}, "
#           f"50th Percentile (Median): {np.percentile(cb1, 50):.2f}, "
#           f"90th Percentile: {np.percentile(cb1, 90):.2f}")

#     # Fetch images for the entire dataset with the given marker
#     cb = get_specific_imgs(dfb, marker=marker)
#     cb = cb.loc[(cb.Batch == 'Batch1') | (cb.Batch == 'Batch2')].Target_Sharpness_Brenner.values
    
#     lower_thresh, upper_thresh = thresholds[marker]
#     lower_p = np.percentile(cb, lower_thresh)
#     upper_p = np.percentile(cb, upper_thresh)
    
#     filtered_out = np.sum((cb1 < lower_p) | (cb1 > upper_p))
#     total_count = len(cb1)
#     filtered_percentage = (filtered_out / total_count) * 100 if total_count > 0 else 0
    
#     filtered_out_DAPI = np.sum((dfDAPI < lower_p_DAPI) | (dfDAPI > upper_p_DAPI))
#     total_count_DAPI = len(dfDAPI)
#     filtered_percentage_DAPI = (filtered_out_DAPI / total_count_DAPI) * 100 if total_count_DAPI > 0 else 0

#     # Print statistics dynamically for the full dataset
#     print(f"Stats for marker: {marker} (All batches, reps, and cell lines)")
#     print(f"Mean: {np.mean(cb):.2f}, Median: {np.median(cb):.2f}, "
#           f"10th Percentile: {np.percentile(cb, 10):.2f}, "
#           f"50th Percentile (Median): {np.percentile(cb, 50):.2f}, "
#           f"90th Percentile: {np.percentile(cb, 90):.2f}")
    
#     print(f"Threshold Percentiles ({lower_thresh}% - {upper_thresh}%): {lower_p:.2f} - {upper_p:.2f}")
#     print(f"Filtered Out: {filtered_out}/{total_count} ({filtered_percentage:.2f}%)")
#     print(f"Filtered Out_DAPI: {filtered_out_DAPI}/{total_count_DAPI} ({filtered_percentage_DAPI:.2f}%)")

#     print("-" * 80)  # Separator for readability
In [47]:
show_images(df_metadata, marker='DNA-damage-pH2Ax', show_DAPI=True, batch=2, cell_line='C9orf72-HRE-1008566', rep=2,
            condition='stress', funova=True, samples=5)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

1
Img shape: (1024, 1024)

/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch2/C9orf72-HRE-1008566/panelH/stress/rep2/DNA-damage-pH2Ax/r12c16f42-ch3t1.tiff
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch1/C9orf72-HRE-1008566/panelH/stress/rep2/DAPI/r12c16f42-ch1t1.tiff

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

Img shape: (1024, 1024)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

--------------------------------
2
Img shape: (1024, 1024)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch2/C9orf72-HRE-1008566/panelH/stress/rep2/DNA-damage-pH2Ax/r12c16f20-ch3t1.tiff
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch1/C9orf72-HRE-1008566/panelH/stress/rep2/DAPI/r12c16f20-ch1t1.tiff
Img shape: (1024, 1024)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

--------------------------------
3
Img shape: (1024, 1024)

/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch2/C9orf72-HRE-1008566/panelH/stress/rep2/DNA-damage-pH2Ax/r12c16f52-ch3t1.tiff
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch1/C9orf72-HRE-1008566/panelH/stress/rep2/DAPI/r12c16f52-ch1t1.tiff

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

Img shape: (1024, 1024)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

--------------------------------
4
Img shape: (1024, 1024)

/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch2/C9orf72-HRE-1008566/panelH/stress/rep2/DNA-damage-pH2Ax/r12c16f89-ch3t1.tiff
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch1/C9orf72-HRE-1008566/panelH/stress/rep2/DAPI/r12c16f89-ch1t1.tiff

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

Img shape: (1024, 1024)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

--------------------------------
5
Img shape: (1024, 1024)

WARNING:root:The image shape (1080, 1080) isn't as expected (1024, 1024).
Cropping image to fit
WARNING:root:Removing pixels from the frame (28, 28)

/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch2/C9orf72-HRE-1008566/panelH/stress/rep2/DNA-damage-pH2Ax/r12c16f98-ch3t1.tiff
/home/labs/hornsteinlab/Collaboration/FUNOVA/input/images/raw/Batch1/C9orf72-HRE-1008566/panelH/stress/rep2/DAPI/r12c16f98-ch1t1.tiff
Img shape: (1024, 1024)

--------------------------------

6. % Site survival after Brenner on target channel tiles¶

Percentage out of the tiles that passed the previous filter. In parenthesis are absolute values (if different than the percentages).

In [ ]:
show_site_survival_target_brenner_tiles(df_dapi, df_target, dapi_filter_by_brenner_tiles,
                                 figsize=(6,24), markers=markers)

Statistics About the Processed Files¶

In [ ]:
names = ['Total number of tiles', 'Total number of whole cells']
stats = ['n_valid_tiles_after_tiles_brenner','site_whole_cells_counts_sum','site_cell_count','site_cell_count_sum']
total_sum = calc_total_sums(df_target, df_dapi, stats, markers)
In [ ]:
total_sum.rename(columns={'n_valid_tiles_after_tiles_brenner': 'n_valid_tiles'}, inplace=True)
In [ ]:
plot_marker_data(total_sum, split_by_cell_line=True)
In [ ]:
plot_marker_data(total_sum, split_by_cell_line=False)

Total tiles¶

In [ ]:
total_sum.n_valid_tiles.sum()

Total whole nuclei in tiles¶

In [ ]:
total_sum[total_sum.marker =='DAPI'].site_whole_cells_counts_sum.sum()

Total nuclei in sites¶

In [ ]:
total_sum[total_sum.marker =='DAPI'].site_cell_count.sum()
In [ ]:
show_total_sum_tables(total_sum)

Show Total Tile Counts¶

For each batch, cell line, replicate and markerTotal number of tiles

In [ ]:
to_heatmap = total_sum.rename(columns={'n_valid_tiles':'index'})
plot_filtering_heatmap(to_heatmap, extra_index='marker', vmin=None, vmax=None,
                      xlabel = 'Total number of tiles', show_sum=True, figsize=(6,24))

Show Total Whole Cell Counts¶

For each batch, cell line, replicate and markerTotal number of tiles

In [ ]:
to_heatmap = total_sum.rename(columns={'site_whole_cells_counts_sum':'index'})
plot_filtering_heatmap(to_heatmap, extra_index='marker', vmin=None, vmax=None,
                      xlabel = 'Total number of whole cells', show_sum=True, figsize=(6,24))

Show Cell Count Statistics per Batch¶

In [ ]:
df_no_empty_sites = df_dapi[df_dapi.n_valid_tiles_after_tiles_brenner !=0]
plot_cell_count(df_no_empty_sites, lines_order, custom_palette, y='site_cell_count_sum', 
                title='Cell Count Average per Site (from tiles)', figsize=(16,6))

plot_cell_count(df_no_empty_sites, lines_order, custom_palette, y='site_whole_cells_counts_sum',
                title='Whole Cell Count Average per Site',figsize=(16,6))

plot_cell_count(df_no_empty_sites, lines_order, custom_palette, y='site_cell_count',
               title='Cellpose Cell Count Average per Site',figsize=(16,6))

Show Tiles per Site Statistics¶

In [ ]:
df_dapi.groupby(['cell_line_cond']).n_valid_tiles_after_tiles_brenner.mean()
In [ ]:
plot_catplot(df_dapi, sns.color_palette('colorblind'), reps=reps,x='cell_line', y_title='Valid Tiles Count', x_title='Cell Line', y='n_valid_tiles_after_tiles_brenner', hue='rep',
             height=4, aspect=2)

Show Mean of cell count in valid tiles¶

In [ ]:
# plot_hm(df_dapi, split_by='rep', rows='cell_line', columns='panel', vmax=3)

Assessing Staining Reproducibility and Outliers¶

In [ ]:
# for batch in batches:
#     print(batch)
#     run_calc_hist_new(batch,cell_lines_for_disp, markers, root_directory_raw, root_directory_proc,
#                            hist_sample=10,sample_size_per_markers=10, ncols=4, nrows=1, figsize=(6,2))
#     print("="*30)
In [ ]:
# save notebook as HTML ( the HTML will be saved in the same folder the original script is)
display(Javascript('IPython.notebook.save_checkpoint();'))
os.system(f'jupyter nbconvert --to html tools/preprocessing_tools/qc_reports/qc_report_funova_exp3.ipynb --output {NOVA_HOME}/manuscript/preprocessing_qc_reports/qc_report_funova_exp3_04.03.25.html')
In [ ]: